Are Pretrained Image Matchers Good Enough for SAR-Optical Satellite Registration?

• URL: http://arxiv.org/abs/2604.10217v2
• Date: Tue, 14 Apr 2026 17:01:40 GMT
• Title: Are Pretrained Image Matchers Good Enough for SAR-Optical Satellite Registration?
• Abstract summary: Cross-modal optical-SAR registration is a bottleneck for disaster-response via remote sensing.<n>Modern image matchers are benchmarked almost exclusively on natural-image domains.
• Score: 7.519268719195278
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-modal optical-SAR (Synthetic Aperture Radar) registration is a bottleneck for disaster-response via remote sensing, yet modern image matchers are developed and benchmarked almost exclusively on natural-image domains. We evaluate twenty-four pretrained matcher families--in a zero-shot setting with no fine-tuning or domain adaptation on satellite or SAR data--on SpaceNet9 and two additional cross-modal benchmarks under a deterministic protocol with tiled large-image inference, robust geometric filtering, and tie-point-grounded metrics. Our results reveal asymmetric transfer--matchers with explicit cross-modal training do not uniformly outperform those without it. While XoFTR (trained for visible-thermal matching) and RoMa achieve the lowest reported mean error at $3.0$ px on the labeled SpaceNet9 training scenes, RoMa achieves this without any cross-modal training, and MatchAnything-ELoFTR ($3.4$ px)--trained on synthetic cross-modal pairs--matches closely, suggesting (as a working hypothesis) that foundation-model features (DINOv2) may contribute to modality invariance that partially substitutes for explicit cross-modal supervision. 3D-reconstruction matchers (MASt3R, DUSt3R), which are not designed for traditional 2D image matching, are highly protocol-sensitive and remain fragile under default settings. Deployment protocol choices (geometry model, tile size, inlier gating) shift accuracy by up to $33\times$ for a single matcher, sometimes exceeding the effect of swapping matchers entirely within the evaluated sweep--affine geometry alone reduces mean error from $12.34$ to $9.74$ px. These findings inform both practical deployment of existing matchers and future matcher design for cross-modal satellite registration.

Related papers

• Solution for UCF UrbanTwin V2X-Real Track: Sim-to-Real Urban LiDAR 3D Object Detection [54.03436243843709]
  This report presents a multi-source collaborative training and class-aware fusion framework for Sim2Real 3D detection.<n>The method organizes digital-twin scans, diffusion-redrawn scans, density-stabilized scans, and pedestrian morphology-aligned samples into a unified training pool with complementary roles.
  arXiv  Detail & Related papers  (2026-09-07T15:16:15Z)
• GeoRay: Gauge-Aware Feed-Forward Satellite 3D Reconstruction in the Geodetic Frame [11.043507429399414]
  Method treats perspective-pretrained features, absolute elevation, and monocular and multi-view cues.<n> lightweight ray-consistent adapters make a frozen backbone matchable along native RPC rays.<n>bench, our absolute-frame benchmark of eighteen systems, scores absolute placement without registration or test-reference leakage.
  arXiv  Detail & Related papers  (2026-08-30T09:27:36Z)
• SPARE: Structural Parameter-Free Affinity Regularization for Flow Matching [30.43836162456455]
  Regularizing internal representations has emerged as an effective accelerator, yet existing methods split into two families with complementary costs.<n>We propose Structural.<n>free Affinity Regularization (SPARE), a regularizer that matches the pairwise affinities of intermediate tokens to those of the clean latents.<n>On ImageNet $256 times 256$ with SiT backbones under matched 400K-iteration budgets, SPARE adds no encoder, head, or parameters and only 0.08 GB of training memory.
  arXiv  Detail & Related papers  (2026-08-03T09:50:51Z)
• Representation Distribution Matching for One-Step Visual Generation [107.84208192304455]
  We elucidate the design space of Representation Distribution Matching (RDM)<n>We identify two design axes, how the distributions are compared and the representations they are compared in, and controlled studies along them yield three findings.<n>The same recipe post-trains the four-step FLUX.2 [klein] into a one-step generator, surpassing the four-step version on GenEval, 0.826 to 0.794, and on PickScore, 22.76 to 22.58, in 90 H200 GPU-hours.
  arXiv  Detail & Related papers  (2026-07-02T16:15:38Z)
• Mind the Gap: Standard 3DGS Evaluation Primarily Measures Near-Trajectory Interpolation [0.0]
  Standard 3D Gaussian Splatting evaluation holds out every N-th frame -- but these frames have trained neighbors on both sides, so the metric measures near-trajectory rather than spatial generalization.<n>We introduce a fair matched-count protocol that isolates this effect.<n>Both arms train on the same number of images and differ only in whether the holdout is spread evenly (interpolation) or forms a contiguous spatial sector (extrapolation)
  arXiv  Detail & Related papers  (2026-07-02T00:24:15Z)
• Self-Supervised Cross-Modal Learning for Image-to-Point Cloud Registration [22.360139236823155]
  CrossI2P is a self-supervised framework that unifies cross-modal learning and two-stage registration in a single end-to-end pipeline.<n>We show that CrossI2P outperforms state-of-the-art methods by 23.7% on the KITTI Odometry benchmark and by 37.9% on nuScenes.
  arXiv  Detail & Related papers  (2025-09-19T11:29:22Z)
• ANROT-HELANet: Adverserially and Naturally Robust Attention-Based Aggregation Network via The Hellinger Distance for Few-Shot Classification [4.283774189998499]
  We introduce ANROT-HELANet, an Adversarially and Naturally RObusT Hellinger Aggregation Network.<n>Our approach implements an adversarially and naturally robust Hellinger distance-based feature class aggregation scheme.<n>Our approach achieves superior image reconstruction quality with a FID score of 2.75, outperforming traditional VAE (3.43) and WAE (3.38) approaches.
  arXiv  Detail & Related papers  (2025-09-14T11:44:43Z)
• Semi-supervised Multiscale Matching for SAR-Optical Image [5.25009884148204]
  We propose a semi-supervised multiscale matching for SAR-optical image matching (S2M2-SAR)<n>Specifically, we pseudo-label those unlabeled SAR-optical image pairs with pseudo ground-truth similarity heatmaps.<n>We also introduce a cross-modal feature enhancement module trained using a cross-modality mutual independence loss.
  arXiv  Detail & Related papers  (2025-08-11T09:55:39Z)
• Zero-shot Inexact CAD Model Alignment from a Single Image [53.37898107159792]
  A practical approach to infer 3D scene structure from a single image is to retrieve a closely matching 3D model from a database and align it with the object in the image.<n>Existing methods rely on supervised training with images and pose annotations, which limits them to a narrow set of object categories.<n>We propose a weakly supervised 9-DoF alignment method for inexact 3D models that requires no pose annotations and generalizes to unseen categories.
  arXiv  Detail & Related papers  (2025-07-04T04:46:59Z)
• Pseudo-triplet Guided Few-shot Composed Image Retrieval [20.040511832864503]
  Composed Image Retrieval (CIR) is a challenging task that aims to retrieve the target image with a multimodal query. We propose a novel two-stage pseudo triplet guided few-shot CIR scheme, dubbed PTG-FSCIR. In the first stage, we propose an attentive masking and captioning-based pseudo triplet generation method, to construct pseudo triplets from pure image data. In the second stage, we propose a challenging triplet-based CIR fine-tuning method, where we design a pseudo modification text-based sample challenging score estimation strategy.
  arXiv  Detail & Related papers  (2024-07-08T14:53:07Z)
• LIP-Loc: LiDAR Image Pretraining for Cross-Modal Localization [0.9562145896371785]
  We apply Contrastive Language-Image Pre-Training to the domains of 2D image and 3D LiDAR points on the task of cross-modal localization. Our method outperforms state-of-the-art recall@1 accuracy on the KITTI-360 dataset by 22.4%, using only perspective images. We also demonstrate the zero-shot capabilities of our model and we beat SOTA by 8% without even training on it.
  arXiv  Detail & Related papers  (2023-12-27T17:23:57Z)
• Noisy-Correspondence Learning for Text-to-Image Person Re-identification [50.07634676709067]
  We propose a novel Robust Dual Embedding method (RDE) to learn robust visual-semantic associations even with noisy correspondences. Our method achieves state-of-the-art results both with and without synthetic noisy correspondences on three datasets.
  arXiv  Detail & Related papers  (2023-08-19T05:34:13Z)
• AffineGlue: Joint Matching and Robust Estimation [74.04609046690913]
  We propose AffineGlue, a method for joint two-view feature matching and robust estimation. AffineGlue selects potential matches from one-to-many correspondences to estimate minimal models. Guided matching is then used to find matches consistent with the model, suffering less from the ambiguities of one-to-one matches.
  arXiv  Detail & Related papers  (2023-07-28T08:05:36Z)
• Modality-Aware Triplet Hard Mining for Zero-shot Sketch-Based Image Retrieval [51.42470171051007]
  This paper tackles the Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) problem from the viewpoint of cross-modality metric learning. By combining two fundamental learning approaches in DML, e.g., classification training and pairwise training, we set up a strong baseline for ZS-SBIR. We show that Modality-Aware Triplet Hard Mining (MATHM) enhances the baseline with three types of pairwise learning.
  arXiv  Detail & Related papers  (2021-12-15T08:36:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.